U.S. Natural Renewable

Bio: U.S. Natural Renewable is an academic researcher. The author has contributed to research in topics: Photovoltaic system. The author has an hindex of 1, co-authored 1 publications receiving 74 citations.

Rooftop solar photovoltaic potential in cities: how scalable are assessment approaches?

Abstract: Distributed photovoltaics (PV) have played a critical role in the deployment of solar energy, currently making up roughly half of the global PV installed capacity. However, there remains significant unused economically beneficial potential. Estimates of the total technical potential for rooftop PV systems in the United States calculate a generation comparable to approximately 40% of the 2016 total national electric-sector sales. To best take advantage of the rooftop PV potential, effective analytic tools that support deployment strategies and aggressive local, state, and national policies to reduce the soft cost of solar energy are vital. A key step is the low-cost automation of data analysis and business case presentation for structure-integrated solar energy. In this paper, the scalability and resolution of various methods to assess the urban rooftop PV potential are compared, concluding with suggestions for future work in bridging methodologies to better assist policy makers.

Distributed Generation Market Demand Model (dGen): Documentation

Abstract: NOTICE This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. The Distributed Generation Market Demand (dGen) model is a geospatially rich, bottom-up, market-penetration model that simulates the potential adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the continental United States through 2050. The National Renewable Energy Laboratory (NREL) developed dGen to analyze the key factors that will affect future market demand for distributed solar, wind, storage, and other DER technologies in the United States within a single modeling platform. The dGen model builds on, extends, and provides significant advances (Table ES-1) over NREL's SolarDS model (Denholm et al. 2009), which is now deprecated. Currently, dGen simulates the adoption of distributed solar (the dSolar module) and distributed wind (the dWind module), as described in detail in Appendices A and B, respectively. The dGen team will add modules in FY16 for behind-the-meter storage (dStorage) as well as a module for evaluating distributed geothermal systems (dGeo), such as ground-source heat pumps and geothermal direct use. The model is also configured to link with utility scale capacity expansion models maintained and applied at NREL (see Appendix C). All technologies modeled within the dGen framework leverage a database of highly resolved geospatial information (Figure ES-1), along with algorithms for modeling DER economics, customer decision-making, and diffusion of technology over time. dGen uses several high-resolution data sets, such as 2012 average electric rates by county. Examples of other high resolution data sets supporting the model include wind resource (200 m resolution), solar resource (10 km resolution), and local and state policy incentives. In dGen, market diffusion of DER technologies is simulated in two-year intervals from 2014 through 2050 based …

Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities

Abstract: We estimate the technical potential of rooftop solar photovoltaics (PV) for select US cities by combining light detection and ranging (lidar) data, a validated analytical method for determining rooftop PV suitability employing geographic information systems, and modeling of PV electricity generation. We find that rooftop PV's ability to meet estimated city electricity consumption varies widely—from meeting 16% of annual consumption (in Washington, DC) to meeting 88% (in Mission Viejo, CA). Important drivers include average rooftop suitability, household footprint/per-capita roof space, the quality of the solar resource, and the city's estimated electricity consumption. In addition to city-wide results, we also estimate the ability of aggregations of households to offset their electricity consumption with PV. In a companion article, we will use statistical modeling to extend our results and estimate national rooftop PV technical potential. In addition, our publically available data and methods may help policy makers, utilities, researchers, and others perform customized analyses to meet their specific needs.